AI Briefing#huggingface
The latest AI and developer news about #huggingface, with the original source and a short summary.
The latest AI and developer news about #huggingface, with the original source and a short summary.
Using SetFit, a new aspect-based sentiment analysis (ABSA) framework was released that outperforms LLMs even with small amounts of data.
HuggingFace kept the base model in memory and dynamically swapped only LoRA adapters, improving inference speed by 300%.
Hugging Face has launched the Optimum-NVIDIA library, which speeds up LLM inference by up to 28x on NVIDIA platforms.
Hugging Face announced optimized support for running LLMs on AMD Instinct GPUs without any separate code modifications.
The abnormally low model scores observed on the DROP benchmark in the Open LLM Leaderboard were found to be caused by a normalization error in the evaluation metric.
With LCM LoRA, SDXL models can generate images quickly using just 4-8 steps without any separate retraining.
Prodigy-HF, a plugin that directly connects the data annotation tool Prodigy with Hugging Face, has been released.
It provides a method for optimizing and deploying Llama 2 models on AWS Inferentia2 using Hugging Face's optimum-neuron.
Hugging Face has introduced a Storage Regions feature in the Enterprise Hub for regulatory compliance and improved performance.
Introduces how to build a personal coding assistant optimized for a specific codebase using Hugging Face's public repositories, along with HugCoder.
With Renumics Spotlight, you can interactively visualize and analyze Hugging Face datasets with a single line of code.
It introduces how to apply fp16 and use memory-efficient attention to speed up inference and reduce memory usage for the SDXL model.
By reproducing OpenAI's RLHF code, key technical details to watch out for when implementing PPO were compiled.
It guides how to deploy high-performance embedding models via Hugging Face Inference Endpoints.
Replit has released its new code generation model, Replit Code V1.5 3B, on Hugging Face.
ONNX Runtime supports over 130,000 models on Hugging Face, accelerating inference performance.
Hugging Face has introduced a Chat Template feature that automatically applies the chat format used during model training.
This guide explains how to deploy the popular AI application AI Comic Factory to your own Space using Hugging Face's Inference API.
Hugging Face has released benchmark results for over 60 configurations to optimally deploy Llama 2 on Amazon SageMaker.
Hugging Face has launched 'Inference for PRO,' offering PRO subscribers a dedicated API for high-performance models with higher usage limits.
Rocket Money successfully deployed a BERT model with more than 4,000 classes into production using the Hugging Face Inference API.
Hugging Face released a leaderboard comparing the performance of object detection models and explained the key evaluation metrics.
Covers quantization, Flash Attention, and architectural innovations for efficient production deployment of LLMs.
It covers how to fine-tune the Llama 2 70B model using PyTorch FSDP while solving memory and checkpoint saving issues.